1 Executive Summary

abc ## Goal of the study abc ## Data ## Findings

2 Data Preperation

2.1 Data scraping

2.2 Data processing

combine lists into single dataframe and clean dataframe

converting billions/millions/thousands into actual numbers

% changes over time of everything on IS and useful financial ratios

joining interest rates to table

finding stock and VIX info

joining file with industry of companies

calculating change in interest rate, stock price, and VIX

3 Exploratory Data Analysis

industry count of stocks

## # A tibble: 12 x 2
##    Sector                 count
##  * <chr>                  <int>
##  1 Communication Services    23
##  2 Consumer Discretionary    60
##  3 Consumer Staples          30
##  4 Energy                    22
##  5 Financials                63
##  6 Health Care               62
##  7 Industrials               69
##  8 Information Technology    66
##  9 Materials                 25
## 10 Real Estate               29
## 11 Utilities                 28
## 12 <NA>                       4

graph of log stock price by quarter and industry

## $title
## [1] "Log Stock Price by Quarter and Industry"
## 
## attr(,"class")
## [1] "labels"

4 Modelling

4.1 VIF

4.2 LASSO

4.3 Linear Regression

4.4 Random Forest

5 Model Comparison